1. Technical Field
The subject matter described herein relates to systems and methods for anticipating and detecting falls of subjects.
2. Background Art
Falls are a grave problem for the elderly and others prone to falling. The American Council on Exercise estimates that 35%-45% of persons 65 year of age or older fall down at least once per year. Injuries, which may be serious, even fatal, are commonly associated with falling. Some current solutions for fall detection allow a person who has fallen to manually press a button for assistance. However, injuries and other effects of falls may leave a person unable to seek or obtain help. For example, a person who is unconscious or dazed and unaware of their surroundings may be incapable of utilizing existing solutions to seek help. Although falls are often regarded as accidents, the incidence of falls differs from a Poisson distribution, and this suggests causal factors are involved. The current state of the art in fall detection and aids lacks mechanisms for detecting falls and issuing warnings as falls occur or before falls occur. The current state of the art also lacks mechanisms for correlating causal factors of falls with fall detection techniques to improve the efficacy thereof.
The reporting of falls in the art is often inaccurate as people frequently have difficulty remembering details about causes of many falls. The current state of the art in fall detection and aids lacks mechanisms for tracking and reporting causal factors of falls and fall-specific data and details for treatment and prediction.